## `summarise()` has grouped output by 'sub', 'nquestion', 'round_text'. You can override using the `.groups` argument.
## Joining, by = "sub"
## Joining, by = "sub"
| Prescan Performance | |||
|---|---|---|---|
| sub | correct1 | conf | conf_correct2 |
| 01 | 0.750 | 0.333 | 0.333 |
| 02 | 0.667 | 0.417 | 0.417 |
| 03 | 0.792 | 0.500 | 0.458 |
| 04 | 0.667 | 0.625 | 0.500 |
| 05 | 0.250 | 0.500 | 0.083 |
| 06 | 0.708 | 0.667 | 0.583 |
| 07 | 0.708 | 0.333 | 0.333 |
| 08 | 0.333 | 0.333 | 0.333 |
| 09 | 0.792 | 0.458 | 0.458 |
| 10 | 0.792 | 0.458 | 0.458 |
| 11 | 0.708 | 0.458 | 0.458 |
| 12 | 0.833 | 0.583 | 0.583 |
| 13 | 0.583 | 0.917 | 0.542 |
| 14 | 0.167 | 0.458 | 0.125 |
| 15 | 0.750 | 0.542 | 0.458 |
| 16 | 0.750 | 0.500 | 0.417 |
| 17 | 0.458 | 0.708 | 0.417 |
| 18 | 0.625 | 0.708 | 0.542 |
| 19 | 0.792 | 0.583 | 0.583 |
| 20 | 0.750 | 0.625 | 0.542 |
| 21 | 0.708 | 0.458 | 0.458 |
| 22 | 0.625 | 0.625 | 0.542 |
| 23 | 0.625 | 0.375 | 0.292 |
| 24 | 0.625 | 0.583 | 0.542 |
| 25 | 0.625 | 0.500 | 0.333 |
| 26 | 0.750 | 0.667 | 0.667 |
| 27 | 0.542 | 0.500 | 0.417 |
|
1
accuracy < 0.5 are highlighted
2
confidence correct < 0.25 are highlighted
|
|||
| Scan Performance | |||
|---|---|---|---|
| sub | correct1 | conf | conf_correct2 |
| 01 | 0.925 | 0.025 | 0.025 |
| 02 | 0.850 | 0.750 | 0.725 |
| 03 | 0.650 | 0.225 | 0.175 |
| 04 | 0.950 | 0.950 | 0.925 |
| 05 | 0.175 | 0.300 | 0.125 |
| 06 | 0.775 | 0.725 | 0.675 |
| 07 | 0.750 | 0.000 | 0.000 |
| 08 | 0.475 | 0.725 | 0.450 |
| 09 | 0.975 | 0.575 | 0.575 |
| 10 | 0.875 | 0.625 | 0.625 |
| 11 | 0.900 | 0.800 | 0.775 |
| 12 | 0.800 | 0.900 | 0.750 |
| 13 | 0.900 | 0.925 | 0.850 |
| 14 | 0.375 | 0.700 | 0.350 |
| 15 | 0.225 | 0.250 | 0.175 |
| 16 | 0.300 | 0.300 | 0.125 |
| 17 | 0.875 | 0.550 | 0.550 |
| 18 | 0.750 | 0.600 | 0.600 |
| 19 | 0.975 | 0.950 | 0.925 |
| 20 | 0.650 | 0.475 | 0.425 |
| 21 | 0.850 | 0.700 | 0.700 |
| 22 | 0.925 | 0.850 | 0.850 |
| 23 | 0.400 | 0.025 | 0.000 |
| 24 | 0.800 | 0.800 | 0.750 |
| 25 | 0.800 | 0.375 | 0.325 |
| 26 | 0.950 | 0.825 | 0.825 |
| 27 | 0.825 | 0.750 | 0.725 |
|
1
accuracy < 0.5 are highlighted
2
confidence correct < 0.25 are highlighted
|
|||
| Mean accuracy per subject | |
|---|---|
| sub | m1 |
| 01 | 0.9375 |
| 02 | 0.9375 |
| 03 | 1.0000 |
| 04 | 1.0000 |
| 05 | 0.3125 |
| 06 | 1.0000 |
| 07 | 0.9375 |
| 08 | 1.0000 |
| 09 | 1.0000 |
| 10 | 1.0000 |
| 11 | 1.0000 |
| 12 | 1.0000 |
| 13 | 0.8125 |
| 14 | 0.8125 |
| 15 | 1.0000 |
| 16 | 0.8750 |
| 17 | 1.0000 |
| 18 | 1.0000 |
| 19 | 1.0000 |
| 20 | 1.0000 |
| 21 | 1.0000 |
| 22 | 1.0000 |
| 23 | 0.8125 |
| 24 | 0.9375 |
| 25 | 1.0000 |
| 26 | 1.0000 |
| 27 | 1.0000 |
|
1
accuracy < 0.5 are highlighted
|
|
Excluding subject 05, 15, and 16. Should we exclude sub 08, 14 and 23?
Notice: sub1/3/7 have very low confidence.
Participant were instructed to answer the expected destination for 3 times during the route: once at Same, once at Overlapping, and once at non-overlapping. They also indicated their confidence towards the choice (sure vs. unsure).
## `summarise()` has grouped output by 'round_text'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'nquestion', 'round'. You can override using the `.groups` argument.
ANVOA for Accuracy:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 26 6.092196 2.047375e-02 * 0.097898186
## 2 nquestion 1 26 114.612245 5.030047e-11 * 0.607837688
## 3 round:nquestion 1 26 1.193687 2.846075e-01 0.008432888
ANVOA for Confidence:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 26 5.280829 2.985704e-02 * 0.06523548
## 2 nquestion 1 26 156.082319 1.707492e-12 * 0.76696572
## 3 round:nquestion 1 26 2.600000 1.189377e-01 0.01069966
ANVOA for high confidence accuracy:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 26 13.980361 9.198922e-04 * 0.12230859
## 2 nquestion 1 26 177.228162 4.060864e-13 * 0.81346422
## 3 round:nquestion 1 26 4.752266 3.850425e-02 * 0.01814002
t-test for mean (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$m
## t = -2.1665, df = 26, p-value = 0.03962
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.288704795 -0.007591502
## sample estimates:
## mean of the differences
## -0.1481481
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$m
## t = -1.5497, df = 26, p-value = 0.1333
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.23694985 0.03324615
## sample estimates:
## mean of the differences
## -0.1018519
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$m
## t = -2.8421, df = 26, p-value = 0.008604
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.12764719 -0.02050096
## sample estimates:
## mean of the differences
## -0.07407407
t-test for confidence (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$conf
## t = 0.21433, df = 26, p-value = 0.832
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.07954248 0.09806100
## sample estimates:
## mean of the differences
## 0.009259259
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$conf
## t = -2.954, df = 26, p-value = 0.006579
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.32974854 -0.05914035
## sample estimates:
## mean of the differences
## -0.1944444
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$conf
## t = -1.7719, df = 26, p-value = 0.08813
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.16000534 0.01185719
## sample estimates:
## mean of the differences
## -0.07407407
t-test for high confidence accuracy (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$cor_conf
## t = -0.23826, df = 26, p-value = 0.8135
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.08914072 0.07062220
## sample estimates:
## mean of the differences
## -0.009259259
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$cor_conf
## t = -3.6931, df = 26, p-value = 0.001035
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.34590648 -0.09853796
## sample estimates:
## mean of the differences
## -0.2222222
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$cor_conf
## t = -2.8844, df = 26, p-value = 0.007777
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.19029185 -0.03193038
## sample estimates:
## mean of the differences
## -0.1111111
Accuracy per round:
## `summarise()` has grouped output by 'round'. You can override using the `.groups` argument.
Distribution of picture index:
Grouped in 10:
## `summarise()` has grouped output by 'npic_10', 'route'. You can override using the `.groups` argument.
Grouped in 5:
## `summarise()` has grouped output by 'npic_5', 'route'. You can override using the `.groups` argument.
Every picture:
## `summarise()` has grouped output by 'npic', 'route'. You can override using the `.groups` argument.
Average accuracy = 0.9661458
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.